1. Field of the Invention
The invention relates generally to the field of image processing. More specifically, the invention relates to real time processing of massive sensor data streams of still imagery, video imagery, thermal imagery, multispectral imagery, hyperspectral imagery, Light Detection and Ranging (LIDAR) imagery and radar imagery. Attributes in the scene are identified by running a plurality of processing algorithms on the image data which are in the form of convolutions on spatial, temporal, and color contents of images and which, with subsequent cross-modal correlations which emulate the image processing of the human visual path consisting of eye, retina, and cortex based processing functions. The invention produces object detections, object tracks, object classifications, and activity recognition and interpretations with negligible latencies. Data analysts and surveillance system operators are not able to provide timely and effective results from these data streams using traditional analytical methods and tools.
2. Description of the Related Art
Military and commercial users have been developing imaging sensors for over forty years as a means to recognize targets based unique features of their signatures in images. These sensors have high data output capable of quickly overwhelming the capacity of current communication links Prior art attempts have partially solved this problem through processing and reporting on a limited set of feature signatures and recording all data for later post-mission analysis. For example, such techniques as Automatic Target Recognition (ATR) require extensive spatial models representing many possible views of targets to be detected and recognized. These techniques have never produced satisfactory probabilities of detection with acceptable levels of false detections. Other techniques based on some degree of modeling of neural systems exploit neural networks which must trained by analyzing extensive data sets to recognize targets or to recognize anomalous images. These techniques likewise have not performed well over the wide variety of imaging conditions that can occur. Modern learning methods, while making a contribution to the problem posed, have not produced acceptable timely and effective image data processing and exploitation.
It would be hugely beneficial to integrate herein a sensor data processor for use in the sensor suite that significantly increases the timeliness and effectiveness of the data processing, exploitation, and dissemination.
The invention permits the optimization and operational deployment of a processor utilizing cognitive image processing principles which analyzes sensor outputs and annotates regions of potential threat or regions having pre-determined characteristics equally at the same rate as the sensor is producing data.
The invention enhances the performance of analysts by significantly reducing the time required for assessment and distribution of results and improving the probability of potential threat and threat activity detection, prioritization, and operator/analyst alerting.